22,162 research outputs found

    MatchPy: A Pattern Matching Library

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    Pattern matching is a powerful tool for symbolic computations, based on the well-defined theory of term rewriting systems. Application domains include algebraic expressions, abstract syntax trees, and XML and JSON data. Unfortunately, no lightweight implementation of pattern matching as general and flexible as Mathematica exists for Python Mathics,MacroPy,patterns,PyPatt. Therefore, we created the open source module MatchPy which offers similar pattern matching functionality in Python using a novel algorithm which finds matches for large pattern sets more efficiently by exploiting similarities between patterns.Comment: arXiv admin note: substantial text overlap with arXiv:1710.0007

    Verifying Recursive Active Documents with Positive Data Tree Rewriting

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    This paper proposes a data tree-rewriting framework for modeling evolving documents. The framework is close to Guarded Active XML, a platform used for handling XML repositories evolving through web services. We focus on automatic verification of properties of evolving documents that can contain data from an infinite domain. We establish the boundaries of decidability, and show that verification of a {\em positive} fragment that can handle recursive service calls is decidable. We also consider bounded model-checking in our data tree-rewriting framework and show that it is \nexptime-complete

    Efficient Pattern Matching in Python

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    Pattern matching is a powerful tool for symbolic computations. Applications include term rewriting systems, as well as the manipulation of symbolic expressions, abstract syntax trees, and XML and JSON data. It also allows for an intuitive description of algorithms in the form of rewrite rules. We present the open source Python module MatchPy, which offers functionality and expressiveness similar to the pattern matching in Mathematica. In particular, it includes syntactic pattern matching, as well as matching for commutative and/or associative functions, sequence variables, and matching with constraints. MatchPy uses new and improved algorithms to efficiently find matches for large pattern sets by exploiting similarities between patterns. The performance of MatchPy is investigated on several real-world problems

    An Implementation of Nested Pattern Matching in Interaction Nets

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    Reduction rules in interaction nets are constrained to pattern match exactly one argument at a time. Consequently, a programmer has to introduce auxiliary rules to perform more sophisticated matches. In this paper, we describe the design and implementation of a system for interaction nets which allows nested pattern matching on interaction rules. We achieve a system that provides convenient ways to express interaction net programs without defining auxiliary rules

    An Automated Social Graph De-anonymization Technique

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    We present a generic and automated approach to re-identifying nodes in anonymized social networks which enables novel anonymization techniques to be quickly evaluated. It uses machine learning (decision forests) to matching pairs of nodes in disparate anonymized sub-graphs. The technique uncovers artefacts and invariants of any black-box anonymization scheme from a small set of examples. Despite a high degree of automation, classification succeeds with significant true positive rates even when small false positive rates are sought. Our evaluation uses publicly available real world datasets to study the performance of our approach against real-world anonymization strategies, namely the schemes used to protect datasets of The Data for Development (D4D) Challenge. We show that the technique is effective even when only small numbers of samples are used for training. Further, since it detects weaknesses in the black-box anonymization scheme it can re-identify nodes in one social network when trained on another.Comment: 12 page

    Assessing the Frontiers of Ultra-Poverty Reduction: Evidence from CFPR/TUP, an Innovative Program in Bangladesh

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    This paper uses household panel data to provide robust evidence on the effects of BRAC’s Targeting the Ultra-poor Program in Bangladesh. Our identification strategy exploits type-1 errors in assignment, comparing households correctly included with those incorrectly excluded, according to program criteria. Evidence from difference-in-difference matching and sensitivity analysis shows that participation had significant positive effects on income, food consumption and security, household durables, and livestock, but no robust impact on health, ownership of homestead land, housing quality and other productive assets. Using quantile difference-in-difference, we find that the income gains from program participation is smaller for the lowest two deciles.Ultra-poor, CFPR/TUP, BRAC, Bangladesh, Microfinance, Bangladesh, Assignment Error, Difference-in-Difference, Matching, Heteroskedasticity-Based Identification

    Deep learning in remote sensing: a review

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    Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

    Search and Result Presentation in Scientific Workflow Repositories

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    We study the problem of searching a repository of complex hierarchical workflows whose component modules, both composite and atomic, have been annotated with keywords. Since keyword search does not use the graph structure of a workflow, we develop a model of workflows using context-free bag grammars. We then give efficient polynomial-time algorithms that, given a workflow and a keyword query, determine whether some execution of the workflow matches the query. Based on these algorithms we develop a search and ranking solution that efficiently retrieves the top-k grammars from a repository. Finally, we propose a novel result presentation method for grammars matching a keyword query, based on representative parse-trees. The effectiveness of our approach is validated through an extensive experimental evaluation
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